Moreover hierarchy is also reflected in the degree-degree correlation through the conditional probability \(P(k|k^{\prime })\) , i.e. the probability that a vertex of degree \(k^{\prime }\)  is linked to a vertex of degree \(k\) . This function is difficult to measure, due to statistical fluctuations, and it is usually substituted by the Average Nearest Neighbors Degree (ANND), defined as \(\left\langle k_{nn}(k)\right\rangle =\sum _{k^{\prime }}k^{\prime }P(k^{\prime }|k)\)  [1]}. For independent networks this quantity would result independent of \(k\) . In analogy with ANND, if we use nodes' strength instead of degree, we obtain the Average Nearest Neighbor Strength (ANNS). Figure REF  report ANND (ANNS) for the two networks, showing the dependency on the vertex's degree (strength) and indicating that, in both cases, highly connected vertexes tend to connect to poorly connected ones, i.e. they show a disassortative behavior. In the scatter plots we also relate this behavior with countries POP end rGDPpc: bigger countries (by both economic and demographic indicators) have in general higher levels of degree (strength), thus connecting to smaller ones with higher ANND (ANNS). In Table REF  we also report the general assortativity coefficient for the networks, confirming our finding.
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